Proinflammatory cytokine and chemokine modulation by<i>Streptococcus suis</i>in a whole-blood culture system
Bibliographic record
Abstract
Streptococcus suis is an important swine and human pathogen. Inflammation, a hallmark of S. suis infection, is thought to be responsible for most clinical signs of meningitis, septicaemia and sudden death. In this work, using a porcine whole blood model, S. suis serotype 2 was shown to trigger the release of several pro-inflammatory cytokines as evaluated by reverse transcriptase-PCR and enzyme-linked immunosorbent assay. Although individual variations were observed among different S. suis strains, no correlations were observed between the strain origin/phenotype and cytokine levels. Live bacteria induced higher tumour necrosis factor alpha, interleukin-1 beta (IL-1beta) and IL-6 levels than did heat-killed bacteria. In contrast, heat-killed bacteria stimulated higher levels of IL-8 and monocyte chemotactic protein one (MCP-1). The bacterial cell wall was observed to be the major cytokine-inducting components, whereas capsule expression was important for MCP-1 activation. The presence of specific antibodies suppressed bacterial growth resulting in significantly reduced levels of cytokine production. Thus, antibody-mediated bacterial phagocytosis combined with suppressed inflammation may be beneficial for infection control strategies. We provide first evidence of S.suis-induction of pro-inflammatory swine cytokines and demonstrate the strength and relevance of the whole blood culture systems in the investigation of S. suis modulation of cytokine production.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".